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Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

The construction sector has significantly evolved in recent decades, in parallel with a huge increase in the amount of data generated and exchanged in any construction project. These data need to be managed in order to complete a successful project in terms of quality, cost and schedule in the the context of a safe project environment while appropriately organising many construction documents.

However, the origin of these data is very diverse, mainly due to the sector’s characteristics. Moreover, these data are affected by uncertainty, complexity and diversity due to the imprecise nature of the many factors involved in construction projects. As a result, construction project data are associated with large, irregular and scattered datasets.

The objective of this chapter is to introduce an approach based on a fuzzy multi-dimensional model and on line analytical processing (OLAP) operations in order to manage construction data and support the decision-making process based on previous experiences. On one hand, the proposal allows for the integration of data in a common repository which is accessible to users along the whole project’s life cycle. On the other hand, it allows for the establishment of more flexible structures for representing the data of the main tasks in the construction project management domain. The incorporation of this fuzzy framework allows for the management of imprecision in construction data and provides easy and intuitive access to users so that they can make more reliable decisions.

The aim of this paper is to provide a unique perspective on entrepreneurship by examining how different ways of understanding entrepreneurial orientation (EO) affect business performance (BP). The study uses a five-dimensional approach to understand EO’s relationship with BP.

A personal survey of key informants (who have decision-making power in their firm), one each from 550 North Indian firms has been conducted. The hypotheses were tested using confirmatory factor analysis and structural equation modeling.

The results indicate that both uni-dimensional and multi-dimensional conceptualizations of EO are equally valid and have a significant impact on BP. The study highlights the contextual nature of the relationship between EO and BP.

This study supports a comprehensive five-dimensional approach to EO, benefiting researchers and management practitioners. It validates an integrated measurement of BP and advances entrepreneurship theories, enabling broader generalizations for improved decision-making and strategy development.

The study is relevant for researchers and management practitioners. This study supports the five-dimensional conceptualization of EO and reveals the relevance of both uni-dimensional and multi-dimensional conceptualizations of EO. The study also lends support to the integrated approach of BP measurement. The results may also help to generalize entrepreneurship theories.

New product development team learning is important in today’s turbulent and uncertain markets and technologies. However, the literature treats team learning as a single construct, ignoring its multi‐dimensionality. In this study, we develop a multi‐dimensional team learning framework based on socio‐cognitive constructs. By studying 124 new product development projects, we show empirically that learning in new product development is best conceived as a multi‐dimensional structure with nine correlated but distinct constructs including: information acquisition, information implementation, information dissemination, unlearning, thinking, improvisation, memory, intelligence and sensemaking. Further, we demonstrate that a model based on the multi‐dimensionality of team learning provides a more robust explanation of new product success than does a unidimensional team learning model.

This chapter reports on a rapidly growing trend in data analysis – analytic comparisons between baseline models and explanatory models. Baseline models estimate values for the dependent variable in the absence of hypothesized causal effects. Thus, the baseline models discussed in this chapter differ from the baseline models commonly used in sequential regression analyses.Baseline modelling entails iteration: (1) Researchers develop baseline models to capture key patterns in the empirical data that are independent of the hypothesized effects. (2) They compare these patterns with the patterns implied by their explanatory models. (3) They use the derived insights to improve their explanatory models. (4) They iterate by comparing their improved explanatory models with modified baseline models.The chapter draws on methodological literature in economics, applied psychology, and the philosophy of science to point out fundamental features of baseline modelling. Examples come from research in international business and management, emerging market economies and developing countries.Baseline modelling offers substantial advantages for theory development. Although analytic comparisons with baseline models originated in some research fields as early as the 1960s, they have not been widely discussed or applied in international management. Baseline modelling takes a more inductive and iterative approach to modelling and theory development. Because baseline modelling holds substantial potential, international-management scholars should explore its opportunities for advancing scientific progress.

Presents the results of a numerical simulation of measured heat transfer through a region surrounding a buried structure. The model applied in the study is a widely used whole building thermal simulation program of a type which predicts the thermal response of structures for building services requirements. A multi‐dimensional numerical conductive heat transfer module has been added to this program but this does not specifically address earth‐contact heat flows. This work attempts to assess the accuracy of the overall package when predicting earth‐coupled heat transfer. It is common practice in the field of building services not to use specific earth‐contact models and so it is important to assess the likely errors thus involved. The predictions of the finite‐volume model are compared with one year of data from a basement test facility. The results are analysed using the Differential Sensitivity Analysis method and an attempt is made to correlate predictive errors with periods of rainfall and snow coverage. It seems that a purely conductive model may be capable, given accurate input data, of satisfactorily predicting the transient temperature variations in the soil/concrete envelope surrounding this structure for the period of the year when no snow coverage is present. However, if one is to accurately model regions of earth‐contact (particularly at shallow depths) in a climate in which rainfall and snow are significant then these influences should be explicitly modelled.

This study aims to present a model that can be used for predicting effective knowledge sharing behaviors in cross‐functional project teams.

Drawn from the extant literature, a coopetitive model of knowledge sharing is postulated. Data from 115 project managers are used to test the proposed model, using partial least squares (PLS).

The findings confirm the applicability and predictive power of the proposed model. Three dimensions of cross‐functional cooperation (cooperative task orientation, cooperative communication, and cooperative interpersonal relationships) were proved to directly drive effective knowledge sharing behaviors. The results show that competition affects effective knowledge sharing behaviors through influencing cooperative behaviors. In addition, this study shows that different dimensions of competition generate mixed impacts. Competition for tangible resources was found to positively affect cooperative communication of individuals, whereas competition for intangible resources (political competition) had negative impacts on cooperative communication and task orientations.

This study contributes to the extant literature by presenting a model that predicts effective knowledge sharing practices in cross‐functional projects. In addition, the results advance the current understanding of the concept and modeling of coopetitive knowledge sharing.

The proposed model of this study can be used by managers in order to facilitate problematic knowledge sharing processes within cross‐functional teams.

This study stands as one of the first attempts in providing a model that explains the forces behind effective knowledge sharing behaviors in cross‐functional teams. The model explores coopetition effect in a systematic way, which has not been previously studied.

The development of a theoretical model for predicting the combustion, performance and emission characteristics of a cylinder head porous medium engine becomes necessary due to imposed requirements from the viewpoint of power, efficiency and toxic gases in the exhaust. The cylinder head porous medium engine was found to have superior combustion, performance and emission characteristics when compared to a conventional diesel engine. The paper aims to discuss these issues.

Due to heterogeneous and transient operation of diesel engine under conventional and porous medium mode, the combustion process becomes complex, and achieving a pure analytical solution to the problem was difficult. Although, closer accuracy of correlation between the computer models and the experimental results is improbable, the computer model will give an opportunity to quantify the combustion and heat transfer processes and thus the performance and emission characteristics of an engine.

In this research work, a theoretical model was developed to predict the combustion, performance and emission characteristics of a cylinder head porous medium engine through two-zone combustion modeling technique, and the results were validated through experimentation.

The two-zone model developed by using programming language C for the purpose of predicting combustion, performance and emission characteristics of a porous medium engine is the first of its kind.

Acme Inc, a large retailer, explores the use of Data warehouse for addressing their decision support infrastructure Challenges. Acme plans for a pilot study to assess the feasibility and evaluate the business benefits of using Data warehouse. The focus of this case is to ascertain the steps involved in design, development and implementation of a Data warehouse.